{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a4b27965-ec86-467c-8e9f-80939b256aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import numpy\n",
    "from matplotlib import pyplot as plt\n",
    "import sklearn.tree\n",
    "\n",
    "numpy.random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "750dc0f4-bce6-4b23-9988-ccbecd2a3df3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_app_one_hot(dataframe):\n",
    "    \"\"\"\n",
    "    Bins age into Young, Adult\n",
    "    Converts to categorical variables\n",
    "    \"\"\"\n",
    "\n",
    "    column_age = \"Age\"\n",
    "    bins_age = [0, 18, float(\"inf\")] # Adult in U.S.\n",
    "    labels_age = ['Young', 'Adult'] # labels must be one larger\n",
    "\n",
    "    dataframe_p = dataframe.copy()\n",
    "    # Bins age into Young, Adult\n",
    "    dataframe_p[column_age] = pandas.cut(dataframe[column_age], bins_age, labels = labels_age) # labels keyword necessary\n",
    "    # Converts to categorical variables\n",
    "    dataframe_p = pandas.get_dummies(dataframe_p, prefix_sep='_') # defaults to all columns\n",
    "\n",
    "    return dataframe_p\n",
    "\n",
    "def process_app(dataframe):\n",
    "    \"\"\"\n",
    "    Keeps age numeric\n",
    "    Converts Platform to categorical\n",
    "    Keeps App intact\n",
    "    \"\"\"   \n",
    "    columns_bin = [\"Platform\"]\n",
    "\n",
    "    dataframe_p = dataframe.copy()\n",
    "    dataframe_p = pandas.get_dummies(dataframe, columns=columns_bin, prefix_sep='_')\n",
    "\n",
    "    return dataframe_p\n",
    "\n",
    "dict_data = {\n",
    "    'Platform': ['iPhone','iPhone','Android','iPhone','Android','Android'],\n",
    "    'Age': [15, 25, 32, 35, 12, 14],\n",
    "    'App': ['Atom Count', 'Check Mate Mate', 'Beehive Finder', 'Check Mate Mate', 'Atom Count', 'Atom Count']\n",
    "    }\n",
    "\n",
    "data_app = pandas.DataFrame(dict_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c95c6e3-baf2-4409-af76-da1f68a9bbb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Platform</th>\n",
       "      <th>Age</th>\n",
       "      <th>App</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>iPhone</td>\n",
       "      <td>15</td>\n",
       "      <td>Atom Count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>iPhone</td>\n",
       "      <td>25</td>\n",
       "      <td>Check Mate Mate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Android</td>\n",
       "      <td>32</td>\n",
       "      <td>Beehive Finder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>iPhone</td>\n",
       "      <td>35</td>\n",
       "      <td>Check Mate Mate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Android</td>\n",
       "      <td>12</td>\n",
       "      <td>Atom Count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Android</td>\n",
       "      <td>14</td>\n",
       "      <td>Atom Count</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Platform  Age              App\n",
       "0   iPhone   15       Atom Count\n",
       "1   iPhone   25  Check Mate Mate\n",
       "2  Android   32   Beehive Finder\n",
       "3   iPhone   35  Check Mate Mate\n",
       "4  Android   12       Atom Count\n",
       "5  Android   14       Atom Count"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e92fd2ab-651b-4e22-b936-6b7cf8d7bdf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## With all categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "32f4e762-a0a3-4dcd-8d8e-62b58beb08fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Platform_Android</th>\n",
       "      <th>Platform_iPhone</th>\n",
       "      <th>Age_Young</th>\n",
       "      <th>Age_Adult</th>\n",
       "      <th>App_Atom Count</th>\n",
       "      <th>App_Beehive Finder</th>\n",
       "      <th>App_Check Mate Mate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Platform_Android  Platform_iPhone  Age_Young  Age_Adult  App_Atom Count  \\\n",
       "0                 0                1          1          0               1   \n",
       "1                 0                1          0          1               0   \n",
       "2                 1                0          0          1               0   \n",
       "3                 0                1          0          1               0   \n",
       "4                 1                0          1          0               1   \n",
       "5                 1                0          1          0               1   \n",
       "\n",
       "   App_Beehive Finder  App_Check Mate Mate  \n",
       "0                   0                    0  \n",
       "1                   0                    1  \n",
       "2                   1                    0  \n",
       "3                   0                    1  \n",
       "4                   0                    0  \n",
       "5                   0                    0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_app_one_hot = process_app_one_hot(data_app)\n",
    "\n",
    "columns_features = ['Platform_iPhone','Platform_Android','Age_Adult','Age_Young']\n",
    "columns_labels = ['App_Atom Count','App_Beehive Finder','App_Check Mate Mate']\n",
    "\n",
    "X = data_app_one_hot[columns_features]\n",
    "y = data_app_one_hot[columns_labels]\n",
    "\n",
    "data_app_one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "201707f9-7a8b-4d5c-b1b7-125046d21a3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_categorical = sklearn.tree.DecisionTreeClassifier()\n",
    "model_categorical.fit(X,y)\n",
    "model_categorical.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "00a96ab8-b3c7-42d4-943d-525d530a14fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.6, 0.8333333333333334, 'Age_Young <= 0.5\\ngini = 0.407\\nsamples = 6\\nvalue = [[3, 3]\\n[5, 1]\\n[4, 2]]'),\n",
       " Text(0.4, 0.5, 'Platform_Android <= 0.5\\ngini = 0.296\\nsamples = 3\\nvalue = [[3, 0]\\n[2, 1]\\n[1, 2]]'),\n",
       " Text(0.2, 0.16666666666666666, 'gini = 0.0\\nsamples = 2\\nvalue = [[2, 0]\\n[2, 0]\\n[0, 2]]'),\n",
       " Text(0.6, 0.16666666666666666, 'gini = 0.0\\nsamples = 1\\nvalue = [[1, 0]\\n[0, 1]\\n[1, 0]]'),\n",
       " Text(0.8, 0.5, 'gini = 0.0\\nsamples = 3\\nvalue = [[0, 3]\\n[3, 0]\\n[3, 0]]')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sklearn.tree.plot_tree(model_categorical, feature_names = columns_features, filled=True, rounded=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "71627165-2a4e-48fa-a77b-b3a3042ff611",
   "metadata": {},
   "outputs": [],
   "source": [
    "## With age as a numerical feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "82f49d3b-5129-4f6a-855a-7670fcdf3f56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>App</th>\n",
       "      <th>Platform_Android</th>\n",
       "      <th>Platform_iPhone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>Atom Count</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>Check Mate Mate</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>Beehive Finder</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>Check Mate Mate</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>Atom Count</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>Atom Count</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age              App  Platform_Android  Platform_iPhone\n",
       "0   15       Atom Count                 0                1\n",
       "1   25  Check Mate Mate                 0                1\n",
       "2   32   Beehive Finder                 1                0\n",
       "3   35  Check Mate Mate                 0                1\n",
       "4   12       Atom Count                 1                0\n",
       "5   14       Atom Count                 1                0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_app_p = process_app(data_app)\n",
    "\n",
    "columns_features = ['Age','Platform_iPhone','Platform_Android']\n",
    "features = data_app_p[columns_features].values\n",
    "labels = data_app_p['App'].values\n",
    "\n",
    "data_app_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7f0bc58-5978-40a1-a62f-34b032208879",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_app = sklearn.tree.DecisionTreeClassifier()\n",
    "model_app.fit(features, labels)\n",
    "model_app.score(features, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8f24d843-bd6f-4e1f-b4ba-af719704e9c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.4, 0.8333333333333334, 'Age <= 20.0\\ngini = 0.611\\nsamples = 6\\nvalue = [3, 1, 2]'),\n",
       " Text(0.2, 0.5, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0, 0]'),\n",
       " Text(0.6, 0.5, 'Platform_iPhone <= 0.5\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
       " Text(0.4, 0.16666666666666666, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
       " Text(0.8, 0.16666666666666666, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sklearn.tree.plot_tree(model_app, feature_names=columns_features, filled=True, rounded=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35ae110b-225c-44d9-901d-89ab881857d0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
